Generative AI in Procurement: Rethinking Bid Evaluation, Fairness and Transparency in Engineering and Construction Contracts
Trine University, Michigan, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 3551-3567
Publication history:
Received on 18 October 2024; revised on 21 December 2024; accepted on 28 December 2024
Abstract:
Procurement in engineering and construction is a complex process characterized by high-value contracts, multiple stakeholders, and significant risks associated with bias, inefficiency, and lack of transparency. Traditional bid evaluation methods often rely on manual assessments and legacy scoring systems that are susceptible to inconsistencies, subjective judgments, and limited capacity to process vast amounts of unstructured data. These limitations undermine fairness and erode trust among bidders and contracting authorities. In recent years, digital procurement systems have sought to address these challenges, yet their capabilities remain constrained by rule-based automation and narrow analytic tools. Generative Artificial Intelligence (AI) offers transformative potential by rethinking how bid evaluation, fairness, and transparency are operationalized in engineering and construction contracts. Through natural language processing, generative models can analyze unstructured proposal documents, extract latent insights, and generate standardized summaries that reduce human subjectivity. They can also simulate evaluation scenarios to test the robustness of scoring criteria, highlight inconsistencies, and flag potential conflicts of interest. Moreover, generative AI enables adaptive fairness mechanisms, incorporating multi-criteria decision-making frameworks that balance cost, quality, sustainability, and social impact in procurement outcomes. This paper explores the integration of generative AI into procurement workflows, emphasizing its potential to accelerate evaluation timelines, improve accountability, and enhance equitable participation among contractors. At the same time, it critically examines risks such as algorithmic bias, data confidentiality, and compliance with regulatory standards. By offering a balanced analysis of opportunities and challenges, the study positions generative AI as a pivotal enabler of transparent, fair, and future-ready procurement in engineering and construction.
Keywords:
Generative AI; Procurement; Bid evaluation; Transparency; Fairness; Construction contracts
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0